MORE：同时进行多视图三维物体识别与姿态估计
MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation
AI总结：
针对机器人多视图识别与姿态估计分离处理及虚拟相机视角固定的问题，提出MORE方法，利用深度物体无关熵估计模型预测最佳视点，同时预测目标物体的姿态和类别标签，并在真实场景中验证了其有效性。
摘要：
同时进行物体识别与姿态估计是机器人安全地与人类及环境交互的两项关键功能。尽管物体识别和姿态估计都使用视觉输入，但大多数最先进的方法将它们作为两个独立的问题来处理，因为前者需要视图不变表示，而物体姿态估计则需要视图相关描述。如今，多视图卷积神经网络（MVCNN）方法展示了最先进的分类性能。尽管MVCNN物体识别已被广泛探索，但关于多视图物体姿态估计方法的研究却很少，同时解决这两个问题的研究更是寥寥无几。MVCNN方法中虚拟相机的姿态通常是预先定义的，这限制了此类方法的应用。在本文中，我们提出了一种能够同时处理物体识别和姿态估计的方法。具体而言，我们开发了一个深度物体无关熵估计模型，能够预测给定三维物体的最佳视点。然后将获得的物体视图输入网络，以同时预测目标物体的姿态和类别标签。实验结果表明，从这些位置获得的视图具有足够的描述性，能够达到良好的准确率得分。此外，我们设计了一个真实的“端饮料”服务场景，以展示所提出的方法在真实机器人任务中的表现。代码可在以下网址在线获取：github.com/SubhadityaMukherjee/more_mvcnn
英文摘要：
Simultaneous object recognition and pose estimation are two key functionalities for robots to safely interact with humans as well as environments. Although both object recognition and pose estimation use visual input, most state-of-the-art tackles them as two separate problems since the former needs a view-invariant representation while object pose estimation necessitates a view-dependent description. Nowadays, multi-view Convolutional Neural Network (MVCNN) approaches show state-of-the-art classification performance. Although MVCNN object recognition has been widely explored, there has been very little research on multi-view object pose estimation methods, and even less on addressing these two problems simultaneously. The pose of virtual cameras in MVCNN methods is often predefined in advance, leading to bound the application of such approaches. In this paper, we propose an approach capable of handling object recognition and pose estimation simultaneously. In particular, we develop a deep object-agnostic entropy estimation model, capable of predicting the best viewpoints of a given 3D object. The obtained views of the object are then fed to the network to simultaneously predict the pose and category label of the target object. Experimental results showed that the views obtained from such positions are descriptive enough to achieve a good accuracy score. Furthermore, we designed a real-life serve drink scenario to demonstrate how well the proposed approach worked in real robot tasks. Code is available online at: github.com/SubhadityaMukherjee/more_mvcnn
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
